Face Recognition

Face recognition with computer vision

Face recognition is the process of identifying one or more people in images or videos by analyzing and comparing patterns. Algorithms for face recognition typically extract facial features and compare them to a database to find the best match. Face recognition is an important part of many biometric, security, and surveillance systems, as well as image and video indexing systems.

Steps in the face recognition workflow.
Steps in the face recognition workflow.

Face recognition leverages computer vision to extract discriminative information from facial images, and pattern recognition or machine learning techniques to model the appearance of faces and to classify them.

You can use computer vision techniques to perform feature extraction to encode the discriminative information required for face recognition as a compact feature vector using techniques and algorithms such as:

  • Dense local feature extraction with SURF, BRISK or FREAK descriptors
  • Histogram of oriented gradients
  • Distance between detected facial landmarks such as eyes, noses, and lips

Machine learning techniques can applied to the extracted features to perform face recognition or classification using:

For more information, see MATLAB®, Computer Vision System Toolbox™, Statistics Toolbox™, and Neural Network Toolbox™.

Examples and How To

Software Reference

See also: MATLAB and OpenCV, machine learning, object detection, object recognition, feature extraction, stereo vision, optical flow, RANSAC, pattern recognition